================================================================================
  REPLICATION PACKAGE
================================================================================

  Title:   Disentangling the Three Facets of Mass Ideological Polarization:
           A Network Approach across 78 Societies
  Authors: Yufan Guo, Yilang Peng, Tian Yang
  Journal: Public Opinion Quarterly


================================================================================
  OVERVIEW
================================================================================

This package contains all materials needed to replicate the analyses, figures,
and tables reported in the main text and Supplementary Information (SI) of the
paper. The core script (replication.R) is organized to match the document
structure of the paper section by section.


================================================================================
  FILE DESCRIPTIONS
================================================================================

  replication.R
      Main replication script (R). Reproduces every figure and table in the
      paper and SI, in document order. See the "Document Map" at the top of
      the script for the full list of outputs. The script is divided into two
      paths: (A) re-run the computationally intensive network estimation loops
      from scratch (Section 4; ~30-90 minutes), or (B) load the pre-computed
      scores from dimension_summary.csv (Section 5; recommended for most users)
      and proceed directly to figure replication.

  dimension_summary.csv
      Pre-computed country-level scores for all three manifestations of mass
      ideological polarization and their alternative measures:
        - disagreement          (Tastle-Wierman ordinal dispersion)
        - symbolic_op_align     (InDegree centrality of the IDEOLOGY node)
        - within_op_align       (sum of edge weights in the 34-item network)
        - density               (network density; SI Section F robustness)
      Loading this file allows users to skip the network estimation loops and
      replicate all figures and tables in a few minutes.

  countrycode.xlsx
      Country-level metadata and covariates. Two sheets:
        Sheet1 ("Sheet1"):  Maps the WVS numeric country identifier (cntry_AN)
                            to human-readable country names.
        Sheet2 ("Sheet2"):  Country-level covariates used in the regression
                            models (Table 3, Figure 5):
                              HDI, polstability, population (log),
                              pressfreedom, ethnicfract, polparrel, minterest,
                              gdp_log (for SI Section G robustness),
                              and V-Dem merge keys (country_text_id, year).

  EVS_WVS_Joint_Rdata_v5_0.rdata
      Joint EVS/WVS Wave 7 survey microdata (2017-2022), version 5.0.0.
      This file is NOT included in the archive because it is publicly
      available for download at:
        https://www.worldvaluessurvey.org/WVSEVSjoint2017.jsp
      Download the "R Data" format of the "Joint EVS/WVS 2017-2022 Dataset"
      (Dataset Version 5.0.0) and place it in the same folder as replication.R.

  V-Dem-CY-Full+Others-v14.rds
      V-Dem Country-Year full dataset, version 14.
      This file is NOT included in the archive because it is publicly
      available for download at:
        https://v-dem.net/data/the-v-dem-dataset/
      Download "V-Dem-CY-Full+Others-v14.rds" and place it in the same folder
      as replication.R.

  README.txt
      This file.


================================================================================
  SYSTEM REQUIREMENTS
================================================================================

  Software:   R (version 4.5.1)
  Platform:   Windows

================================================================================
  DOCUMENT MAP: OUTPUTS PRODUCED BY replication.R
================================================================================

  MAIN TEXT
    Figure 1  — Simulation: Correlation vs. Partial Correlation Network
                (Figure1_H1_panel_A_correlation.png,
                 Figure1_H1_panel_B_partial_correlation.png)
    Figure 3  — World maps: global distribution of the three manifestations
                (Figure3_maps.png)
    Figure 4  — Scatter plots between the three manifestations
                (Figure4_scatter_manifestations.png)
    Table 2   — Top 10 societies in each manifestation
                (printed to console)
    Figure 5  — OLS regression coefficient plot
                (Figure5_coefplot.png)
    Figure 6  — Scatter plots: HDI vs. each manifestation
                (Figure6_HDI_scatter.png)
    Table 3   — OLS models predicting Disagreement
                (printed to console)

  SUPPLEMENTARY INFORMATION
    Section A — Full item list (Table A1) and country list (Table A2)
                (printed to console)
    Section B — Robustness: Excluding governmental items
                (SI_Figure_B1_coefplot_reduced_items.png)
    Section C — Country-level variable descriptions
                (printed to console)
    Section D — Robustness: Country-year as unit of analysis
                (SI_Figure_D1_coefplot_country_year.png)
    Section E — Measurement validation
                (SI_Figure_E1_measure_correlations.png,
                 SI_Figure_E2_external_validation.png)
    Section F — Robustness: Within-operational alignment by network density
                 SI_Figure_F2_density_map.png,
                 SI_Figure_F3_density_coefplot.png)
    Section G — Robustness: Modernization by GDP per capita
                (SI_Figure_G1_GDP_coefplot.png,
                 SI_Figure_G2_GDP_scatter.png)
    Section H — Simulation detail
                (Figure1_H1_panel_A_correlation.png,
                 Figure1_H1_panel_B_partial_correlation.png)
    Section I — Robustness: Weighted results
                (SI_Figure_I1_scatter_weighted.png,
                 SI_Figure_I2_coefplot_weighted.png;
                 SI Table I1 printed to console)

